A Digitally Capable Aged Care Workforce: Demands and Directions for Workforce Education and Development
Bibliographic record
Abstract
As the aged care sector undergoes digital transformation, greater attention is needed to development of digital health capability in its workforce. There are many gaps in our understanding of the current and future impacts of technology on those who perform paid and unpaid aged care work. Research is needed to understand how to make optimal use of both digital resources and human resources for better aged care. In this Viewpoint, we reflect on a workshop held during an international conference that identified shared concepts and concerns to shape further research into workforce capability. Digital technologies and digital data can increase quality of care in a system that operates through partnerships among service providers, service users, and community members. To realize this potential, digital health learning and development are needed in the aged care workforce. As digital dimensions of aged care services expand, the sector needs clearer direction to implement approaches to workforce learning and development. These must be appropriate to support the safe and ethical performance of care work and to increase the satisfaction of those who care and those for whom they care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".